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Model-driven analysis of metabolic immunomodulators of macrophage activation

ABSTRACT: Model-driven multi-omic data analysis of the RAW 264.7 cell line elucidates metabolic immunomodulators of macrophage activation

OTHER RELATED OMICS DATASETS IN: PRJNA171843MTBLS23PRJNA152753

INSTRUMENT(S): LTQ Orbitrap, LTQ

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Raw-264.7 Cell

SUBMITTER: Matthew Monroe

LAB HEAD: Bernard Palsson

PROVIDER: PXD003055 | Pride | 2015-11-11

REPOSITORIES: Pride

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Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation.

Bordbar Aarash A Mo Monica L ML Nakayasu Ernesto S ES Schrimpe-Rutledge Alexandra C AC Kim Young-Mo YM Metz Thomas O TO Jones Marcus B MB Frank Bryan C BC Smith Richard D RD Peterson Scott N SN Hyduke Daniel R DR Adkins Joshua N JN Palsson Bernhard O BO

Molecular systems biology 20120626

Macrophages are central players in immune response, manifesting divergent phenotypes to control inflammation and innate immunity through release of cytokines and other signaling factors. Recently, the focus on metabolism has been reemphasized as critical signaling and regulatory pathways of human pathophysiology, ranging from cancer to aging, often converge on metabolic responses. Here, we used genome-scale modeling and multi-omics (transcriptomics, proteomics, and metabolomics) analysis to asse ...[more]

PMID: 22735334

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Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation

Project description:We used genome-scale modeling and multi-omics (transcriptomics, proteomics, and metabolomics) analysis to assess metabolic features that are critical for macrophage activation. We constructed a genome-scale metabolic network for the RAW 264.7 cell line to determine metabolic modulators of activation. Metabolites well-known to be associated with immunoactivation (glucose and arginine) and immunosuppression (tryptophan and vitamin D3) were among the most critical effectors. Intracellular metabolic mechanisms were assessed, identifying a suppressive role for de-novo nucleotide synthesis. Finally, underlying metabolic mechanisms of macrophage activation are identified by analyzing multi-omic data obtained from LPS-stimulated RAW cells in the context of our flux-based predictions. Two condition (flagellin and LPS) time course exposure of RAW 264.7 cell line at 1, 2, 4, and 24 hours. Two replicates for each condition and time point. All conditions compared to a pool of untreated cells at a 0 hour time point.

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Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation

Project description:Macrophages are central players in immune response, manifesting divergent phenotypes to control inflammation and innate immunity through release of cytokines and other signaling factors. Recently, the focus on metabolism has been reemphasized as critical signaling and regulatory pathways of human pathophysiology, ranging from cancer to aging, often converge on metabolic responses. Here, we used genome-scale modeling and multi-omics (transcriptomics, proteomics, and metabolomics) analysis to assess metabolic features that are critical for macrophage activation. A genome-scale metabolic network for the RAW 264.7 cell line was constructed to determine metabolic modulators of activation. Metabolites well-known to be associated with immunoactivation (glucose and arginine) and immunosuppression (tryptophan and vitamin D3) were among the most critical effectors. Intracellular metabolic mechanisms were assessed, identifying a suppressive role for de-novo nucleotide synthesis. Finally, underlying metabolic mechanisms of macrophage activation were identified by analyzing multi-omic data obtained from LPS-stimulated RAW cells in the context of our flux-based predictions. This study demonstrates that the role of metabolism in regulating activation may be greater than previously anticipated and elucidates underlying connections between activation and metabolic effectors. This submission corresponds to the metabolomics data from this study.

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